Results 191 to 200 of about 3,693 (219)

Template method hyper-heuristics

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014
The optimization literature is awash with metaphorically-inspired metaheuristics and their subsequent variants and hybridizations. This results in a plethora of methods, with descriptions that are often polluted with the language of the metaphor which inspired them [8].
John R. Woodward, Jerry Swan
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Hyper-heuristics: Autonomous Problem Solvers

2021
Algorithm design is a general task for any problem-solving scenario. For Search and Optimization, this task becomes rather challenging due to the immense algorithm design space. Those existing design options are usually traversed to devise algorithms by the human algorithm development experts together with the specialists on the target problem domains.
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Evolutionary Cross-Domain Hyper-Heuristics

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015
Designing effective algorithms to solve computational problems is difficult and time-consuming. The standard methodology for designing such algorithms is “top-down”. This process breaks down large problems into more understood components and eventually identifies problem-specific operators that algorithms need to use to solve the given problem.
Patricia Ryser-Welch, Julian F. Miller, Shariar Asta   +2 more
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Selection Perturbative Hyper-Heuristics

2018
Selection perturbative hyper-heuristics select which low-level perturbative heuristic to apply at each point of improvement to a given initial complete solution to a problem. The initial solution is usually created either randomly or using a constructive low-level heuristic.
Nelishia Pillay, Rong Qu
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Generation Constructive Hyper-Heuristics

2018
In solving combinatorial optimization problems, a low-level constructive heuristic is used to create an initial solution, which forms a starting point for optimization techniques to solve the problem. These heuristics are problem dependent and are rules of thumb, manually derived based on human intuition.
Nelishia Pillay, Rong Qu
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Markov chain hyper-heuristic (MCHH)

Proceedings of the 13th annual conference on Genetic and evolutionary computation, 2011
In this paper we present the Markov chain Hyper-heuristic (MCHH), a novel online selective hyper-heuristic which employs reinforcement learning and Markov chains to provide an adaptive heuristic selection method. Experiments are conducted to demonstrate the efficacy of the method and comparisons are made with standard heuristics, a random hyper ...
Kent McClymont, Edward C. Keedwell
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Introduction to Hyper-Heuristics

2018
Research into solving combinatorial optimization problems such as timetabling, vehicle routing and rostering problems has involved deriving techniques that improve the results obtained by existing techniques for known benchmark sets. These benchmark sets are made publicly available for performance comparisons of different techniques in solving these ...
Nelishia Pillay, Rong Qu
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A hyper-heuristic clustering algorithm

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012
The so-called heuristics have been widely used in solving combinatorial optimization problems because they provide a simple but effective way to find an approximate solution. These technologies are very useful for users who do not need the exact solution but who care very much about the response time. For every existing heuristic algorithm has its pros
Chun-Wei Tsai, Huei-Jyun Song, Ming-Chao Chiang   +2 more
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Cross-Domain Hyper-Heuristics

2018
Hyper-heuristics aim to provide heuristic algorithms of a higher level of generality that produce good results for all problems in a domain rather than just for one or two problem instances but poor results for the others. Cross-domain hyper-heuristics extend this scope of generality across domains.
Nelishia Pillay, Rong Qu
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Advances in Hyper-Heuristics

2018
The previous chapters have introduced the four types of hyper-heuristics, presented the theoretical foundations and examined various applications of hyper-heuristics. This chapter provides an overview of some advanced topics and recent trends in hyper-heuristics, namely, hybrid hyper-heuristics, hyper-heuristics for automated design, automated design ...
Nelishia Pillay, Rong Qu
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